Filter bank extensions for subject non-specific SSVEP based BCIs

Abstract

Recently, filter bank analysis has been used in several detection methods to extract selective frequency features across multiple brain computer interface (BCI) modalities due to its effectiveness and simple structure. In this work, we propose filter bank technique as a standard preprocessing method for popular training free multi-channel steady-state visual evoked potential (SSVEP) detection methods to overcome subject-specific performance differences and a general improvement in detection accuracy. Our study validates the effectiveness of filter bank extensions by comparing performance differences of multichannel methods with their filter bank counterparts using a forty target SSVEP benchmark dataset collected across thirty five subjects. The results demonstrate that the proposed two stage (a filter bank stage followed by SSVEP detection) implementation of popular multichannel algorithms provide significant improvement in performance at short datalengths of < 2.75 s (p < 0.001) and can be viewed as a potential standard detection approach across all SSVEP identification problems.